{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import (print_function, absolute_import, division,\n",
    "                        unicode_literals, with_statement)\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import datasets\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import ParameterGrid\n",
    "import numpy as np\n",
    "from cleanlab.classification import LearningWithNoisyLabels\n",
    "from cleanlab.noise_generation import generate_noisy_labels\n",
    "from cleanlab.util import value_counts\n",
    "from cleanlab.latent_algebra import compute_inv_noise_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`cleanlab` can be used with any classifier and dataset for multiclass\n",
    "learning with noisy labels. Its comprised of components from the theory and\n",
    "algorithms of **confident learning**. It's a Python class that wraps around\n",
    "any classifier as long as .fit(X, y, sample_weight),\n",
    ".predict(X), .predict_proba(X) are defined.\n",
    "See https://l7.curtisnorthcutt.com/cleanlab-python-package for docs.\n",
    "\n",
    "\n",
    "## Here we show the performance with LogisiticRegression classifier\n",
    "## versus LogisticRegression \\*without\\* cleanlab on the Iris dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seed for reproducibility\n",
    "seed = 2\n",
    "clf = LogisticRegression(solver='lbfgs', multi_class='auto', max_iter=1000)\n",
    "rp = LearningWithNoisyLabels(clf=clf, seed=seed)\n",
    "np.random.seed(seed=seed)\n",
    "\n",
    "# Get iris dataset\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data  # we only take the first two features.\n",
    "y = iris.target\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "try:\n",
    "    get_ipython().run_line_magic('matplotlib', 'inline')\n",
    "    from matplotlib import pyplot as plt\n",
    "\n",
    "    _ = plt.figure(figsize=(12, 8))\n",
    "    color_list = plt.cm.tab10(np.linspace(0, 1, 6))\n",
    "    _ = plt.scatter(X_train[:, 1], X_train[:, 3],\n",
    "                    color=[color_list[z] for z in y_train], s=50)\n",
    "    ax = plt.gca()\n",
    "    #     plt.axis('off')\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    _ = ax.get_xaxis().set_ticks([])\n",
    "    _ = ax.get_yaxis().set_ticks([])\n",
    "    _ = plt.title(\"Iris dataset (feature 3 vs feature 1)\", fontsize=30)\n",
    "except Exception as e:\n",
    "    print(e)\n",
    "    print(\"Plotting is only supported in an iPython interface.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate lots of noise.\n",
    "noise_matrix = np.array([\n",
    "    [0.5, 0.0, 0.0],\n",
    "    [0.5, 1.0, 0.5],\n",
    "    [0.0, 0.0, 0.5],\n",
    "])\n",
    "\n",
    "py = value_counts(y_train)\n",
    "# Create noisy labels\n",
    "s = generate_noisy_labels(y_train, noise_matrix)\n",
    "\n",
    "try:\n",
    "    get_ipython().run_line_magic('matplotlib', 'inline')\n",
    "    from matplotlib import pyplot as plt\n",
    "\n",
    "    _ = plt.figure(figsize=(15, 8))\n",
    "    color_list = plt.cm.tab10(np.linspace(0, 1, 6))\n",
    "    for k in range(len(np.unique(y_train))):\n",
    "        X_k = X_train[y_train == k]  # data for class k\n",
    "        _ = plt.scatter(\n",
    "            X_k[:, 1],\n",
    "            X_k[:, 3],\n",
    "            color=[color_list[noisy_label] for noisy_label in s[y_train == k]],\n",
    "            s=200,\n",
    "            marker=r\"${a}$\".format(a=str(k)),\n",
    "            linewidth=1,\n",
    "        )\n",
    "    _ = plt.scatter(\n",
    "        x=X_train[s != y_train][:, 1],\n",
    "        y=X_train[s != y_train][:, 3],\n",
    "        color=[color_list[z] for z in s],\n",
    "        s=400,\n",
    "        facecolors='none',\n",
    "        edgecolors='black',\n",
    "        linewidth=2,\n",
    "        alpha=0.5,\n",
    "    )\n",
    "    ax = plt.gca()\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    _ = ax.get_xaxis().set_ticks([])\n",
    "    _ = ax.get_yaxis().set_ticks([])\n",
    "    _ = plt.title(\"Iris dataset (features 3 and 1). Label errors circled.\",\n",
    "                  fontsize=30)\n",
    "except Exception as e:\n",
    "    print(e)\n",
    "    print(\"Plotting is only supported in an iPython interface.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WITHOUT confident learning, Iris dataset test accuracy: 0.6\n",
      "\n",
      "Now we show improvement using cleanlab to characterize the noise\n",
      "and learn on the data that is (with high confidence) labeled correctly.\n",
      "\n",
      "WITH confident learning (noise matrix given), Iris dataset test accuracy: 0.83\n",
      "WITH confident learning (noise / inverse noise matrix given), Iris dataset test accuracy: 0.83\n",
      "WITH confident learning noise not given, Iris dataset test accuracy: 0.77\n"
     ]
    }
   ],
   "source": [
    "print('WITHOUT confident learning,', end=\" \")\n",
    "clf = LogisticRegression(solver='lbfgs', multi_class='auto', max_iter=1000)\n",
    "_ = clf.fit(X_train, s)\n",
    "pred = clf.predict(X_test)\n",
    "print(\"Iris dataset test accuracy:\", round(accuracy_score(pred, y_test), 2))\n",
    "\n",
    "print(\"\\nNow we show improvement using cleanlab to characterize the noise\")\n",
    "print(\"and learn on the data that is (with high confidence) labeled correctly.\")\n",
    "print()\n",
    "print('WITH confident learning (noise matrix given),', end=\" \")\n",
    "_ = rp.fit(X_train, s, noise_matrix=noise_matrix)\n",
    "pred = rp.predict(X_test)\n",
    "print(\"Iris dataset test accuracy:\", round(accuracy_score(pred, y_test), 2))\n",
    "\n",
    "print('WITH confident learning (noise / inverse noise matrix given),', end=\" \")\n",
    "inv = compute_inv_noise_matrix(py, noise_matrix)\n",
    "_ = rp.fit(X_train, s, noise_matrix=noise_matrix, inverse_noise_matrix=inv)\n",
    "pred = rp.predict(X_test)\n",
    "print(\"Iris dataset test accuracy:\", round(accuracy_score(pred, y_test), 2))\n",
    "\n",
    "print('WITH confident learning noise not given,', end=\" \")\n",
    "clf = LogisticRegression(solver='lbfgs', multi_class='auto', max_iter=1000)\n",
    "rp = LearningWithNoisyLabels(clf=clf, seed=seed)\n",
    "_ = rp.fit(X_train, s)\n",
    "pred = rp.predict(X_test)\n",
    "print(\"Iris dataset test accuracy:\", round(accuracy_score(pred, y_test), 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Performance of confident learning across varying settings.\n",
    "To learn more, inspect ```cleanlab/pruning.py``` and ```cleanlab/classification.py```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "param_grid = {\n",
    "    \"prune_method\": [\"prune_by_noise_rate\", \"prune_by_class\", \"both\"],\n",
    "    \"converge_latent_estimates\": [True, False],\n",
    "}\n",
    "\n",
    "# Fit LearningWithNoisyLabels across all parameter settings.\n",
    "params = ParameterGrid(param_grid)\n",
    "scores = []\n",
    "for param in params:\n",
    "    clf = LogisticRegression(solver='lbfgs', multi_class='auto', max_iter=1000)\n",
    "    rp = LearningWithNoisyLabels(clf=clf, n_jobs=1, **param)\n",
    "    _ = rp.fit(X_train, s)  # s is the noisy y_train labels\n",
    "    scores.append(accuracy_score(rp.predict(X_test), y_test))\n",
    "\n",
    "# Print results sorted from best to least\n",
    "for i in np.argsort(scores)[::-1]:\n",
    "    print(\"Param settings:\", params[i])\n",
    "    print(\n",
    "        \"Iris dataset test accuracy (using confident learning):\\t\",\n",
    "        round(scores[i], 2),\n",
    "        \"\\n\",\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Param settings: {'prune_method': 'prune_by_class', 'converge_latent_estimates': False}\n",
      "Iris dataset test accuracy (using confident learning):\t 0.83 \n",
      "\n",
      "Param settings: {'prune_method': 'prune_by_class', 'converge_latent_estimates': True}\n",
      "Iris dataset test accuracy (using confident learning):\t 0.83 \n",
      "\n",
      "Param settings: {'prune_method': 'both', 'converge_latent_estimates': False}\n",
      "Iris dataset test accuracy (using confident learning):\t 0.8 \n",
      "\n",
      "Param settings: {'prune_method': 'both', 'converge_latent_estimates': True}\n",
      "Iris dataset test accuracy (using confident learning):\t 0.8 \n",
      "\n",
      "Param settings: {'prune_method': 'prune_by_noise_rate', 'converge_latent_estimates': False}\n",
      "Iris dataset test accuracy (using confident learning):\t 0.77 \n",
      "\n",
      "Param settings: {'prune_method': 'prune_by_noise_rate', 'converge_latent_estimates': True}\n",
      "Iris dataset test accuracy (using confident learning):\t 0.77 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "param_grid = {\n",
    "    \"prune_method\": [\"prune_by_noise_rate\", \"prune_by_class\", \"both\"],\n",
    "    \"converge_latent_estimates\": [True, False],\n",
    "}\n",
    "\n",
    "# Fit LearningWithNoisyLabels across all parameter settings.\n",
    "params = ParameterGrid(param_grid)\n",
    "scores = []\n",
    "for param in params:\n",
    "    clf = LogisticRegression(solver='lbfgs', multi_class='auto', max_iter=1000)\n",
    "    rp = LearningWithNoisyLabels(clf=clf, n_jobs=1, **param)\n",
    "    _ = rp.fit(X_train, s)  # s is the noisy y_train labels\n",
    "    scores.append(accuracy_score(rp.predict(X_test), y_test))\n",
    "\n",
    "# Print results sorted from best to least\n",
    "for i in np.argsort(scores)[::-1]:\n",
    "    print(\"Param settings:\", params[i])\n",
    "    print(\n",
    "        \"Iris dataset test accuracy (using confident learning):\\t\",\n",
    "        round(scores[i], 2),\n",
    "        \"\\n\",\n",
    "    )"
   ]
  }
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